Upload modeling_mp_rna.py with huggingface_hub

modeling_mp_rna.py

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+
+import torch.nn as nn
+from transformers import AutoModel
+
+class CustomMPRNAForSequenceClassification(nn.Module):
+    def __init__(self, base_model, num_labels):
+        super().__init__()
+        self.base_model = base_model
+        self.num_labels = num_labels
+        self.classifier = nn.Linear(base_model.config.hidden_size, num_labels)
+        self.dropout = nn.Dropout(0.1)
+
+    def forward(self, input_ids, attention_mask=None, labels=None):
+        outputs = self.base_model(input_ids=input_ids, attention_mask=attention_mask)
+        pooled_output = outputs[0][:, 0, :]
+        pooled_output = self.dropout(pooled_output)
+        logits = self.classifier(pooled_output)
+
+        loss = None
+        if labels is not None:
+            loss_fct = nn.CrossEntropyLoss()
+            loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
+        return {"logits": logits, "loss": loss} if loss is not None else {"logits": logits}